Mammography Information System

ABSTRACT

A method and system for analyzing and retrieving tissue abnormality tracking data, providing a tool for a radiologist that includes a report summarizing the statistical frequency of diagnosed patients, both locally and nationally, with tissue region-of-interest classifications similar to the tissue images taken of the anatomy of an individual patient. In one embodiment, a computer aided diagnostic program can suggest region-of-interest attributes and classifications for user review. In one embodiment, user can select a seed area for computer aided diagnostic program analysis, the CAD analysis can suggest region-of-interest attributes and classifications. In yet another embodiment, the region-of-interest abnormality attributes suggested by the computer aided diagnostic program can be stored and compared with the user selected abnormality attributes and classifications. In one embodiment the images are of human breast tissue.

RELATED APPLICATIONS

This application is a Continuation-In-Part of U.S. patent applicationSer. No. 12/625,898, filed Nov. 25, 2009, which claims the benefit ofU.S. Provisional Application No. 61/282,000, entitled “MAMMOGRAPHYINFORMATION SYSTEM” and filed Nov. 24, 2009, which are incorporatedherein by reference in its entirety.

The following co-pending patent applications of common assignee containsome common disclosure: “Multiple Modality Mammography Image Gallery andClipping System,” U.S. patent application Ser. No. 12/625,926 and“Mammography Statistical Diagnostic Profiler and Prediction System,”U.S. patent application Ser. No. 12/625,910, filed Nov. 25, 2009, whichare incorporated herein by reference in their entireties. A copy of eachof the above-identified related applications is attached hereto asAppendix A and Appendix B, respectively.

TECHNICAL FIELD

The invention relates to management of images and medical data, and morespecifically to patient data and breast tissue images originating frommultiple modalities.

BACKGROUND

Historically, interpretation and diagnosis of mammograms and othermedical image analysis has been performed using hardcopy x-ray filmsviewed on an alternator that typically allows x-ray films to beilluminated and masked for diagnostic viewing. Newer technology allows aradiologist or other medical professional to view mammograms and otherdiagnostic images electronically on high-resolution monitors. Theseimages can also be digitally stored and transmitted across securenetworks for archiving or review by other professionals.

A radiologist generally begins his or her review process by reviewing apatient's background information relevant to a radiology study, such asa patient's name, age, and any applicable medical conditions or riskfactors. After reviewing the background information, the radiologistviews multiple images created by radiological, X-ray, computedtomography (CT), ultrasound, magnetic resonance imaging (MRI),tomosynthesis, or other imaging technique of the patient's breast, orother organ, and dictates or uses a computerized information system totrack findings, create reports, and make recommendations for futureexaminations. Such findings can include information pertaining to tissuedensity, the presence of masses, cysts, calcifications and otherabnormalities, or any other breast tissue characteristics.

While there has been recent debate regarding the frequency at whichwomen should undergo regular mammogram screenings, and at what age suchscreenings should begin, it is unlikely that the relatively quick andtypically effective practice of mammography screening for breast cancerwill disappear completely. Accordingly, there will continue to be a needfor radiologists to view and interpret the images generated from patientexaminations and screenings. Because the risk of breast cancer threatensthe lives of many women, especially those over age 40, radiologists areoften inundated with large numbers of mammogram images that must beviewed and, if abnormalities are present, categorized in order todetermine if further examination is required. The developments inadvanced patient imaging techniques, such as MRI, are also increasingthe raw number of images that a radiologist can review. Therefore, thereis an ongoing need to improve the speed and efficiency of theradiologist's review of the mammogram images, without sacrificingaccuracy, and with the smallest number of false-positive diagnoses.Additionally, given that mammogram screenings are performedperiodically, such as annually or biannually, once screening begins fora particular individual, there is also a need to manage, track andanalyze data taken over a period of years or decades for thatindividual.

One example of a computerized mammography information system (MIS) toreview patient images is the PenRad Mammography Information Systemavailable from PenRad. This system provides for the digital presentationof patient data.

Legislation has mandated that mammography facilities track positivemammography findings and correlate such findings with biopsy results,maintain statistics for mammography medical outcome and analysis auditson each physician, and provide direct written notification to allpatients of their exam results. The generation and correlation of thisdata is maintained locally by each medical center for each patient.

One system for categorizing this information is the BreastImaging-Reporting and Data System (BI-RADS) published by the AmericanCollege of Radiology (ACR). BI-RADS provides a system of mammographyassessment categories in the form of standardized codes assigned by aradiologist during or after the viewing and interpretation of a medicalimage. BI-RADS allows for concise and unambiguous understanding ofpatient records between multiple radiologists and medical facilities.Consequently, a large number of mammogram images, biopsy results, anddiagnosis statistics are potentially available in a patient-anonymousformat, in compliance with the Health Insurance Portability andAccountability Act of 1996 (HIPAA).

Recently, Digital Imaging and Communications in Medicine (DICOM) systemshave become the accepted format for medical imaging systems. This formatprovides for the distribution and viewing of medical studies and imagesacross a variety of platforms. The use of DICOM has, among other things,enabled industry compatibility and improved workflow efficiency betweenimaging and other information systems located in various healthcareenvironments. Currently, the DICOM standard is an 18-part publication,PS 3.1-2008 through PS 3.18-2008 describing a standard for digitalimaging and communications in medicine developed by the American Collegeof Radiology (ACR) and the National Electrical Manufacturers Association(NEMA), which is hereby incorporated by reference in its entirety. Amongother elements, the DICOM standard provides a method of uniquelynumbering any image or other information object to facilitate theunambiguous identification of images or information objects as they areviewed or manipulated in a system or transported across a network.

Conventional imaging systems enable a DICOM server to provide medicalimages across a network to various DICOM compatible clients on thenetwork. Some examples of DICOM clients include picture archiving andcommunications systems, softcopy workstations, computer-aided diagnosis(CAD) systems, DICOM compatible CD or DVD burners, and other networksystem devices known to those skilled in the art. One example of astandards-based medical imaging environment is disclosed in U.S. Pat.No. 6,909,795, to Tecotzky et al., incorporated herein by reference.

Computer Aided Diagnosis systems, such as the R2 Digital CAD system byHologic, Inc., or systems available from iCAD, Inc. of Nashua, N.H., andMedipattern Corp. of Toronto, Ontario, can produce various image featuredescriptions by extracting attributes of one or more regions of interest(ROI) from a DICOM SR file or other file format containing aradiographic image (e.g. a mammogram). This information can includesize, location and attributes of a region of interest including, but notlimited to, shape, margins, abnormality structure type, echo patterns,and the like. Many of these ROIs and associated features are false marks(i.e., not relevant to a diagnosis), or if the region for processing hasbeen manually been selected for processing may contain variousattributes or features that are not relevant or incorrect. An exemplaryCAD system is disclosed in U.S. Pat. No. 7,783,094 to Collins et al.,incorporated herein by reference.

SUMMARY

Embodiments relate to systems and methods of retrieving and analyzingpatient data in a mammography information system as part of or inconjunction with the diagnosis and interpretation of patient mammographyimages that substantially meet the aforementioned needs of the industry.In an example embodiment, the system is capable of retrieving,presenting, and analyzing patent images originating from a variety ofmodalities.

In an embodiment, a configurable mammography diagnostic system comprisesa plurality of electronic displays, at least one of the plurality ofelectronic displays configured to display a breast tissue image havingat least one region of interest, a database including a plurality ofexisting categorizations of at least one known region of interest in atleast one of a plurality of breast tissue images, a graphical userinterface presented on at least one of the plurality of electronicdisplays and including an anatomical diagram on which the at least oneregion of interest can be marked, a detailing button linked to a screenconfigured to present a plurality of possible characteristics accordingto which a manual current categorization of a region of interest in thebreast tissue image can be defined, and a profiler display buttonconfigured to present statistical information related to a comparison ofthe manual current categorization with the existing categorizations, aclipping tool with which a portion of the breast tissue image displayedon at least one of the plurality of electronic displays can be selectedas a second image, the second image displayable on at least one of theplurality of electronic displays as a subset of the breast tissue image,and a processing engine configured to link the second image to thebreast tissue image, store the second image in an image database, and toassociate the second image with a corresponding region of interestmarked on the anatomical diagram.

In an embodiment, a method for managing patient mammography datacomprises obtaining a plurality of breast tissue images selected fromthe group consisting of an X-ray image, a CT image, an MRI image, anultrasound image, and a pathology image, identifying a region ofinterest in at least one of the plurality of breast tissue images,obtaining a categorization of the region of interest according to anestablished lexicon, comparing the categorization with a database ofexisting categorizations and presenting a diagnostic indicator based onthe comparing, storing a selected region of the at least one of theplurality of breast tissue images as a second image, mapping the secondimage to a storage location of the at least one of the plurality ofbreast tissue images, and associating the selected region with thecategorized region of interest.

In an embodiment, a mammography information system comprises at leastone electronic display, a graphical user interface presented on the atleast one electronic display and configured to present data andinformation related to a patient, the graphical user interfacecomprising an image gallery configured to display thumbnailrepresentations of a plurality of images that form a portion of the dataand information, the plurality of images selectable from X-ray images,CT images, MRI images, ultrasound images, pathology images and documentimages, and a database operable to store the thumbnail representationsof the plurality of images.

In an embodiment, a configurable mammography diagnostic system comprisesa reporting system that automates the importation and selection of oneor more ROI and associated ROI descriptions in an image, generated by aCAD image analysis module of the image, for inclusion in (or exclusionfrom) a narrative report generation and the cataloging of the findingsdescribing each ROI.

In one embodiment, a computer aided diagnostic program can suggestregion-of-interest classifications for user review. In one embodiment,user can select a seed area of an image for computer aided diagnosticprogram analysis, the CAD analysis can suggest region-of-interestattributes and classifications based on the seed area provided by theuser. In yet another embodiment, the region-of-interest abnormalityattributes and classifications suggested by the computer aideddiagnostic program can be stored and compared with the user selectedabnormality attributes and classifications.

The above summary of the invention is not intended to describe eachillustrated embodiment or every implementation of the present invention.The figures and the detailed description that follow more particularlyexemplify these embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the present invention may be more completelyunderstood in consideration of the following detailed description ofvarious embodiments in connection with the accompanying drawings, inwhich:

FIG. 1 is an example mammogram information system (MIS) displayworkstation according an embodiment of the invention.

FIG. 2 is an example of a mammography exam data-form suitable for usewith embodiments of the invention.

FIG. 3 is an example of the mammography exam data-form of FIG. 2indicating a region of interest (ROI).

FIG. 4 is an example of a mammogram image with an ROI indicated.

FIG. 5 is an example of an ultrasound image with an ROI indicated.

FIG. 6 a is another example embodiment of a ROI data entry form for usewith embodiments of the invention.

FIG. 6 b is the ROI data entry form of FIG. 6 a with additional ROIcategorizations entered.

FIG. 6 c depicts two additional exemplary embodiments of ROI data entryforms for use with embodiments of the invention.

FIG. 7 is an example of a form showing the statistical analysis of aROI.

FIG. 8 is an example of a form showing available images that matchstatistical analysis of the ROI of FIG. 7.

FIG. 9 is an example of a form showing a patient's exam history.

FIG. 10 is an example embodiment of a report generated according anembodiment of the invention.

FIG. 11 is an example embodiment of a web-based form for use with anembodiment of the invention.

FIG. 12 is an example embodiment of a web-based form for use with anembodiment of the invention.

FIG. 13 is an example embodiment of a web-based form for use with anembodiment of the invention.

FIG. 14 is an example embodiment of a web-based form for use with anembodiment of the invention.

FIG. 15 is an example of a mammography exam data-form and an example ofa ROI gallery window.

FIG. 16 a is an another depiction of the ROI gallery window of FIG. 15.

FIG. 16 b is another example of a ROI gallery window for use withembodiments of this invention.

FIG. 17 is an example embodiment of a ROI viewer depicting an individualimage for use with embodiments of this invention.

FIG. 18 is an example of an interpretation work-list form for use withembodiments of the invention.

FIG. 19 is an example of a prior examinations form for use withembodiments of this invention.

FIG. 20 is an example of a mammography exam data-form depicting multipleROI.

FIG. 21 is an example of a ROI data entry form and an example of a ROIgallery window.

FIG. 22 is an example of a mammogram image with an ROI-seed indicated.

FIG. 23 is an example of a mammogram image with an ROI-boundaryindicated.

While the present invention is amenable to various modifications andalternative forms, specifics thereof have been shown by way of examplein the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the presentinvention to the particular embodiments described. On the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present invention as definedby the appended claims.

DETAILED DESCRIPTION OF THE FIGURES

In the following detailed description of the various embodiments of thepresent invention, numerous specific details are set forth in order toprovide a thorough understanding of various embodiments of the presentinvention. However, one skilled in the art will recognize that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as to not unnecessarily obscure aspects ofthe various embodiments of the present invention.

This specification includes various embodiments of the present inventiondescribe mammography information systems that may be used for a varietyof functions to diagnose tissue abnormalities. These functions includethe review, analysis, and categorization of images collected by any of avariety of imaging techniques. While the various embodiments of thepresent invention is directed to the review of mammogram images, it willbe understood that, in some embodiments, that images of tissuesdepicting other portions of a human or animal anatomy can also bediagnosed with the aid of an embodiment of the present invention.Examples of other such portions include, but are not limited to,cerebral, vascular, thoracic, or other regions that may benefit frommedical imaging procedures and review by medial or other properlytrained users.

Embodiments provide a computerized mammography information system thatallows for the digital correlation of a wide variety patent data relatedto a mammography image or other breast tissue diagnostic imagingprocedures. An exemplary system is able to electronically track breasttissue abnormalities across multiple image types, provide a customizableinterface for convenient and efficient image review, allow for anindividual user to save preferred image hanging protocols, categorizemultiple imaging types, generate statistics, and provide patientcorrespondence. Additionally, the integration of various computer aideddiagnostic/detection (CAD) protocols for multiple image modalities intothe system assists the medical professional in reviewing and accuratelydiagnosing any abnormalities present in a patient's diagnostic images.This advancement in accuracy also provides the benefit of reducing theneed for expensive and invasive biopsy or surgery due to false positivediagnosis.

Embodiments of the mammography information system provide an efficient,easy to use, and customizable interface for use by a medicalprofessional for the review and analysis of medical images from avariety of source. The system is capable of integrating medical imagesacquired through X-ray, CT, ultrasound, MRI, tomosynthesis, or otherimaging techniques.

The increasing availability and quantity of digital informationrepresenting patient medical data and diagnostic images has created aneed for a system that allows a doctor or radiologist to quickly review,organize, and if necessary retrieve, multiple diagnostic images that maybe indicative of an individual patient's condition. In addition to theavailability of digital mammography images, other patient associateddata, such as biopsy or other test results and even entire medicalhistories or correspondence records can be stored in a digital format.Access to images where the pictured abnormality has been definitivelydiagnosed can assist with the doctor or radiologist's diagnosis of thenew patient's individual condition. Prior to the electronic production,archival, and detailed categorization of patient images, suchcomparisons were limited to a handful of common abnormalities describedin the various medical texts or required laborious manual review ofindividual patient files.

Therefore, there is a need for a system that will quickly allow aradiologist to select a ROI in a mammogram or other image and correlatethe ROI to a mapping or outline of the patient's anatomy in order toimprove efficiency of patient diagnosis and record retrieval including amechanism to “clip” a ROI from any image modality, or form of electronicrecord, and associating that “clipping” with a specific ROI placement inthe patient's record.

Additionally, the availability of this collection of breast tissueimages and their associated biopsy results presents an opportunity forstatistical analysis of the likelihood that a matching region ofinterest (ROI) in an individual patient's mammography images ismalignant or benign and whether or not a biopsy or further imagingshould be ordered. Therefore, there is a need for a system that willquickly allow a radiologist to classify a ROI in a mammogram or otherimage and correlate the ROI to a large pool of existing data samplesthat have been definitively diagnosed in order to improve the accuracyand efficiency of patient diagnosis. The radiologist can be assisted inthe classification of the ROI by a CAD module by automatically detectingpotential ROI abnormalities or simply reducing the number of physical orverbal actions needed by the radiologist to enter the ROI classifyingdata.

In an example embodiment, a MIS is provided for use by a radiologist orother medical professional that preloads all of an individual patient'smedical images for a specific portion of the patient's anatomy,regardless of the modality used to create the images. For example, in abreast cancer screening, any available x-ray, ultrasound, MRI, biopsy,or other images for the patient are retrieved and preprocessed by anappropriate CAD algorithm. A CAD module for the appropriate image typecan isolate one or more ROI for review in an individual image. Thedisclosed invention takes these individual CAD results and correlatesany common ROI findings between images of the same or differentmodalities. A summary “map” or outline of the examined patient's anatomyis then generated and displayed for the medical professional along withany other details about the potential ROI(s) that were generated by theCAD module(s).

The mammography image gallery and clipping system according to thepresent invention provides a convenient organization of all of theimages associated with a ROI, regardless of modality, for presentationto a medical professional. The system stores lower resolution clippings,or thumbnail images, for pathological images, reports, and abnormalitiesfound, and optionally categorized, by radiologists or CAD products at afacility that have been entered into a mammography information system.The system stores low resolution images as well as the reference to theoriginal image and ROI of the original image. As more patients aredefinitively diagnosed and their pathology records updated in thesystem, the larger the collection of abnormality images depicting apreviously diagnosed and imaged condition that become available in thesystem. This system can be integrated into an existing MIS or utilizedas a standalone interface providing access to a large sample ofmammogram abnormality images.

The system also provides an efficient mechanism for creating acomprehensive collection of abnormality data. The collection comprisinga uniform lexicon of classifications that allows for further analysisand study of the data while still maintaining patient privacy asrequired by the applicable law. Those skilled in the art of developingand maintaining electronic databases will appreciate and understand thetradeoffs associated with the storage requirements necessary for theimplementation of the contemplated system. As numerous mammographyfacilities implement this non-patient identifying (and HIPAA compliant)data can be transferred to a central location accumulating a morecomplete database of abnormality images and the correspondingcharacterization of data points for various pathology types.

In an example embodiment, the method of analyzing and retrievingabnormality tracking data provides a report of the statistical frequencyof diagnosed patients both locally and nationally with mammogram ROIclassifications similar to an individual patient. The abnormality datacan include information disclosing the frequency of similar ROIclassifications have been biopsied and the number of biopsies that weremalignant or benign. The disclosed method of capturing and reportingabnormality tracking data provides a radiologist or other medicalprofessional a tool to assess the likelihood of a ROI being malignant orbenign, and whether or not the patient should undergo additionaltesting. The system then presents these statistics to the radiologistwho can then choose to look further into the underlying related data ifhe or she desires.

The statistical mammography predictive system according to the presentinvention provides instantly and continually updated outcome statisticsto a medical professional. The system utilizes the information and datapoints for each and every abnormality found by radiologists at afacility that have been entered into a mammography information system.As more patients are definitively diagnosed and their pathology recordsupdated in the system, the greater the chances that an individualpatient will have a condition similar to a previously diagnosed andimaged condition. This system can be integrated into an existing MIS orutilized as a standalone interface providing access to a large sample ofmammogram abnormality data.

The system also provides an efficient mechanism for creating acomprehensive collection of abnormality data. The collection comprisinga uniform lexicon of classifications that allows for further analysisand study of the data while still maintaining patient privacy asrequired by the applicable law. Only unique copies of each combinationof tracing data points must be kept in the system. As duplicate data isaccumulated the counters of the abnormality and its diagnosis as benignor malignant are incremented. This aggregation of data creates a compactand anonymous abnormality database for the medical location. If desired,a complete reference of all abnormality data can be maintained. Thoseskilled in the art of developing and maintaining electronic databaseswill appreciate and understand the tradeoffs associated with the storagerequirements necessary for the implementation of the contemplatedsystem.

As numerous mammography facilities implement this non-patientidentifying (and HIPAA compliant) data can be transferred to a centrallocation accumulating an more complete database of abnormalities and thecorresponding benign or malignant counters for each combination oftracking points and pathology. Therefore, the large number of recordedabnormalities can be culled down to a manageable set of uniquecombinations specified by radiologists around the country. This culling,or grouping of duplicate abnormalities, allows for a medicalprofessional to access a comprehensive database of the known set ofabnormalities nearly instantaneously.

In a further embodiment, the system disclosed provides a mechanism toevaluate, validate, and improve any of a variety of existing CAD modulesand techniques by providing an efficient platform for testing the cadmodule or technique against a wide variety of known, physicianevaluated, and definitively diagnosed, patient abnormalities or ROI.

The invention can be better understood by reference to FIGS. 1-19. FIG.1 illustrates an example embodiment of a mammogram display workstation100. A typical mammogram display workstation 100 includes a controllerdisplay system 110 and at least one high-resolution image monitor 112.One or more additional high-resolution image monitor units 114 can alsobe used to provide additional viewing area to provide for the comparisonof two or more images at full resolution. The controller display system110 is any of a variety of commonly available video display monitorscoupled to a personal computer such as an IBM-PC or compatible systemrunning a version of the Microsoft WINDOWS operating system, or theequivalent thereof. In an embodiment, the image monitors 112 and 114 areliquid crystal displays (LCDs) that provide high-resolution and enhancedcontrast for ease of viewing images, but may also be a cathode ray tubeor other appropriate display in other embodiments. An exemplary imagemonitor can display approximately 2500×2000 pixels, although a varietyof image monitor sizes are contemplated. In one embodiment, themammogram display workstation 100 includes a server computer (not shown)that runs DICOM communications components of the mammogram displayworkstation 100; alternatively, this DICOM software may run on thecontroller display system 110. In yet another embodiment, a servercomputer is included that runs an Archived Image Retrieval service;alternatively, this software may also run on the controller displaysystem 110 or on the DICOM compliant server.

The mammogram display workstation 100 includes software that allowsimages to be analyzed using the image processor in the controllerdisplay system 110 to analyze each image of a study set, compare withcomplementary images to generate a suspect list to reduce falseindicators, and to generate graphic overlay images to identify areas ofinterest. When an image is displayed on an image monitor 112 or 114,imaging tools included in the system allow a user working with thesystem to further manipulate an image. These software tools may providemagnification of a desired region of an image; image inversion,reversal, rotation, or other repositioning; image/background colorinversion; noise filtering from images to reduce or eliminate extraneousdata and enhance pertinent image data; customized side-by-side imagecomparisons; and image reorganization, for example.

FIG. 2 illustrates an example embodiment of a medical diagnostic systemthat includes an abnormality-summary window 200. Abnormality-summarywindow 200 provides a convenient patient information summary 210 and aninterface to import or enter additional data. In window 200 theradiologist can enter abnormality data for either the left or rightbreast by clicking on an “Add Abnormality” button 220. Additionally, auser can import a CAD report detailing any abnormalities that have beendetected by existing CAD software. Examples of suitable CAD softwareinclude the CadStream product by Confirma and the B-CAD product byMedipattern, among others.

As shown in FIG. 3, imported CAD information stored in compliance with apre-determined system such as BI-RADS can be used to generate awire-frame map or guide 230 depicting the location and depth of a ROI inor on a patient's anatomy that was detected by the CAD software orentered manually by a radiologist. The density of the patient's tissueis also presented in selector 240. The guide 230 includes both acraniocaudal (CC) view 250 and a mediolateral/oblique (ML) view 260 ofboth the left and right breasts of a patient. The ROI is depicted by thecraniocaudal mark 252 and the mediolateral mark 262. In othersituations, an abnormality may only be visible in one or the other ofthe ML or the CC view and, accordingly, only a single mark would bedisplayed in either the craniocaudal (CC) view 250 or themediolateral/oblique (ML) view 260.

In an embodiment, the ROI data underlying either craniocaudal mark 252or mediolateral mark 262 can be represented as the number of pixelspaces from at least two edges of the original image represented by theROI. The retention of the number of pixels from at least two edgesprovides for the derivation of the location of the ROI on the originalimage. This allows the storage of multiple ROI for a singlehigh-resolution image without the need to store multiple copies of thehigh-resolution image or even high-resolution clippings. It also permitsderivation or mapping of an ROI in one image to other images based onknown pixel sizes and edge distances.

In another alternative embodiment, the data underlying these two marksare used to then calculate an approximate location of the abnormality asviewed by a physician when facing the patient. This calculation alsocompensates for the fact that during the creation of a mammographyimage, the patient's breast is compressed to increase the amount ofviewable tissue in the two-dimensional x-ray image. Additionally,compensation must be made for the angle at which themediolateral/oblique view 260 is taken relative to the craniocaudal view250 during mammogram imaging. Those skilled in the art will appreciatethat the two views are not necessarily created at angles exactlyperpendicular to each other due to the wide variety of patient anatomyand the need to capture as much tissue as possible in each image. Thebreast orientation, size and thickness information is provided alongwith the mammogram image. The resulting combination of the craniocaudaldata and the mediolateral data produce the clock-position 270 as shownfor the exemplary ROI. This calculation is not calculated if the ROI isonly visible on a single image, as both a craniocaudal and mediolateralposition are required, along with a distance either from the patient'snipple or chest wall, to calculate the location of the ROI inthree-dimensional space.

An abnormality does not need to be located or observable in both viewsto be characterized. Often in mammography an abnormality is only seen inone view and additional imaging is conducted to confirm its location inanother view. The additional imaging can also reveal superimposedtissue, a situation in which the breast tissue of several layers wascompressed together causing a potential mass seen in a single image withthe appearance of an actual abnormality. A radiologist viewing multipleimages of the same tissue area can appropriately categorize thesesituations.

Also shown in FIG. 3 is a three-word indication 272 of the approximatelocation of the ROI in the patient's breast. In this example the ROI islocated in the inferior (lower), lateral (outside), middle (distancebetween the chest and nipple) portion of the patient's right breast.Similar terms for the remaining quadrants and depth are provided by theACR guidelines and will be understood by those skilled in the art.

An additional feature of the system is the capability of importing anyROI from a patient's previous examination that are already present inthe system's database. A radiologist or technician can select the “ClonePrev” button 280 to review and import data from a previous examination.This feature further eliminates the need for duplicated effort on thepart of the medical professional conducting the review of the patient'sexam images.

The system is capable of handling a variety of imaging technologies.FIG. 4 depicts an exemplary x-ray generated mammogram image 300 with anROI indicated by a dashed outline 310 on the image 300 of the patient'sbreast tissue 320. FIG. 5 depicts an exemplary ultrasound image 330 withan ROI indicated by a dashed outline 340 on the image 330 of thepatient's breast tissue 350. While the type of information depicted in amammogram image 300 is clearly different from the ultrasound image 330,the system maintains the ROI indicated on each respective image bystoring the coordinates of each ROI as an offset, in one embodimentutilizing the number of pixels, from at least two edges of the originaldigital image, regardless of the technique employed to generate theimage. These coordinates are then used to calculate the distance fromthe patient's chest wall, nipple, or other appropriate reference point,to determine the measurements defining the location of the ROI. Similartechniques can be applied to other imaging technologies such as MRI orCT images that are capable of being stored in a standardized digitalformat where the correlation of the number of pixels in the image to thereal-world distance depicted in the image is known.

FIG. 6 a depicts an embodiment of an abnormality-detailing window 400.The detailing window 400 provides an interface for a radiologist toenter or view the detailed attributes that describe an abnormality in aselected ROI. FIG. 6 a depicts the single attribute 402 of a “Mass” asbeing selected to describe the ROI depicted in FIG. 3. As indicated bythe system, the presence of a mass alone is generally not enough toindicate the presence of a malignancy. The radiologist then selects animpression 404 and an appropriate recommendation in the “Impression &Recs” area 406. In one embodiment, the system suggests an impression orrecommendation in area 406 based on other selected attributes in window400, which can then be reviewed by the radiologist and altered, ifdesired. The system can also dynamically and automatically adjust theselection in area 406 if other attributes in window 400 are changedduring review. In other embodiments, area 406 is selectable by aradiologist or doctor.

The abnormality-detailing window 400 can include a profiler button 410that provides a count of matching abnormalities and their pathologicaloutcome. The profiler button 410, or another appropriate window,displays the number of biopsies performed that were diagnosed asmalignancies 412, the number of biopsies performed that were diagnosedas benign 414, and the total number of matching abnormalities 416 in thedatabase. The sum of the number of malignancies 412 and the number ofbenign 414 is the total number of biopsies performed on abnormalitiespossessing the same attributes selected in detailing window 400 at thatlocation. The second line 418 of profiler window 410 displays these samequantities found in a national database. As discussed above, the singleattribute of a Mass 402 in FIG. 6 a yields a relatively low number ofmalignancies 412 (roughly 1.4%) of similar abnormalities in the localdatabase. The combination of the number of malignancies 412 and thenumber of benign 414 is also a low percentage of the total number ofsimilar abnormalities, indicating a low frequency of requests by thepatient's physician for a biopsy. The profiler button 410 is depicted inthe lower corner of the screen to provide a convenient, yet out of theway area to present statistical information. Other locations orembodiments, such as a floating window that can be repositioned by theradiologist are contemplated.

Two database versions are typically present in every system—one is the“local” version containing the data specific to the medical facilitywhere the system is installed. This local data can be subsequentlyuploaded to a centralized server to be integrated with into a“regional,” “national,” or “global” version of the database. This allowsindividual users to compare their own facility's results with a largersample of results. Additionally, the “local” version can be linked tothe on-site examination image data, allowing the radiologist to seeother examinations related to a specific pathology finding or set ofcharacteristics. The radiologist can then nearly instantly view selectedexaminations, images, or specified regions of interest retrieved fromthe local database. The system can also be configured to link toinformation and retrieve images from the larger databases, although inone embodiment this can be done without any patient identifyinginformation.

FIG. 6 b depicts the abnormality-detailing window 400 of FIG. 6 a, withthree additional characteristics that describe the ROI. The Mass 402 ischaracterized as “Irregular” 420, “Microlobulated” 422, and having a“High density” 424. In the “Impression & Recs” area 406 the addition ofthe “5 Highly suggestive” 426 attribute indicates that a follow-upexamination of the patient is necessary. In this case, the radiologisthas selected the “Ultrasound guided bx” option 428, indicating that therecommended next step for the patient is an ultrasound-guided biopsy ofthe abnormality.

The addition of the three ROI characteristics in FIG. 6 b significantlynarrowed the number of matching abnormalities in the MIS database asshown in the profiler button 410. While only half of the biopsiedabnormalities resulted in a result of malignancy 412 for the localdatabase, as seen in the national database line 418, the vast majorityof biopsied abnormalities of this type were malignant. While therelatively low number of data points presented for this abnormality typemay not be sufficient to draw any definitive conclusions, this exampleshows the utility of being able to compare a local sample with a largermulti-site database of abnormalities providing an indication to thelocal medical personnel that further review of this abnormality scenariomay be required. Those skilled in the medical and radiology arts willappreciate these and other advantages that this collection of data andthe ease of access provided by the system yield.

FIG. 6 c depicts another example of a right breast MRIabnormality-detailing window 440 and an example of an MRIabnormality-dimensioning window 442. These two windows display theBI-RADS compatible data points, optionally generated by a CAD softwarepackage used to pre-evaluate and generate the ROI in the MIS. In oneembodiment, the CAD software package can populate the various fieldspresented by an abnormality window, such as exemplary MRIabnormality-dimensioning window 442. These windows also provide aradiologist with an interface to adjust, re-characterize, correct, orremove the ROI data based on their professional assessment of the ROIdepicted in the patient's images. As depicted, in abnormality-dimensionswindow 442 a radiologist can quickly select or change the radial size,anti-radial size, transverse size, AP size, cranio size, distance fromthe nipple, distance from the skin, and distance from the chest, of theabnormality. Other appropriate measurements or mechanisms for enteringthese values are also contemplated.

The system contemplated in the example embodiment dynamically updatesthe values shown in the profiler button 410, of FIG. 6 b, every time anew attribute is selected in abnormality-detailing window 400. Oneembodiment can achieve this high access speed by assigning an enhancedversion of ACR lexicon descriptors to individual bits in a group ofintegers. This approach also yields a relatively compact database size,further minimizing search time. The tables below provide an exemplarysampling of potential abnormality lexicons. Each item in a lexicon isassigned a value. In Table 1, the STATS_VALUES field first provides aspecified index into a list of database field values. These databasefields are assigned indexes numbered 0 to n−1. The second hexadecimalvalue is the actual value assigned to the individual lexicon item. Whenthis item is selected during an examination, the specified bit value isset in the assigned integer field using a bitwise OR operation. TheLISTBOX_NAME column provides the general description of where on theabnormality-detailing window 440 the attribute would be grouped. TheITEM_NAME column provides the detailed characteristic that a radiologistcan select when characterizing a patent image.

TABLE 1 Mammogram Lexicon Item Detailing LISTBOX_NAME ITEM_NAMESTATS_VALUES Specify Abnormality Fibrocystic tissue 0.0 × 00000001Specify Abnormality Cyst simple 0.0 × 00000002 Specify AbnormalityMastitis area 0.0 × 00000004 Specify Abnormality Mass solid 0.0 ×00000008 Specify Abnormality Lesion 0.0 × 00000010 Specify AbnormalityCyst 0.0 × 00000020 Specify Abnormality Abscess 0.0 × 00000040 SpecifyAbnormality Mass 0.0 × 00000080 Specify Abnormality Papillary lesion  0. × 000000100 Profile Abnormality Irregular 1.0 × 00000001 ProfileAbnormality Lobulated 1.0 × 00000002 Profile Abnormality Oval 1.0 ×00000004 Profile Abnormality Reniform 1.0 × 00000008 Profile AbnormalityRound 1.0 × 00000010 Profile Abnormality Circumscribed 1.0 × 00000020Profile Abnormality Microlobulated 1.0 × 00000040 Profile AbnormalityObscured 1.0 × 00000080 Profile Abnormality Indistinct 1.0 × 00000100Profile Abnormality Spiculated 1.0 × 00000200 Profile AbnormalityIntraductal 1.0 × 00000400 Profile Abnormality Irregular 1.0 × 00000800Profile Abnormality Smooth 1.0 × 00001000 Profile Abnormality Highdensity 1.0 × 00002000 Profile Abnormality Equal density 1.0 × 00004000Size and Distance Parallel/skin 1.0 × 00800000 Size and DistancePerpendic/skin 1.0 × 01000000 Assoc Calcs Generic calcs 2.0 × 00000001Assoc Calcs Amorphous 2.0 × 00000002 Assoc Calcs Branching 2.0 ×00000004 Assoc Calcs Coarse 2.0 × 00000008 Assoc Calcs Dystrophic 2.0 ×00000010 Assoc Calcs Eggshell 2.0 × 00000020 Assoc Calcs Lucent-centered2.0 × 00002000 Assoc Calcs Milk of calcium 2.0 × 00004000 Assoc CalcsPleomorphic 2.0 × 00008000 Assoc Calcs Punctate 2.0 × 00010000 AssocCalcs Rim 2.0 × 00020000 Assoc Calcs Round 2.0 × 00040000 Assoc CalcsSkin 2.0 × 00080000 Assoc Calcs Spherical 2.0 × 00100000 Assoc CalcsSuture 2.0 × 00200000 Assoc Calcs Vascular 2.0 × 00400000 Assoc CalcsClustered 2.0 × 00800000 Assoc Calcs Diffuse 2.0 × 01000000 Assoc CalcsGrouped 2.0 × 02000000 Assoc Calcs Linear 2.0 × 04000000 Assoc CalcsRegional 2.0 × 08000000 Assoc Calcs Scattered 2.0 × 10000000 Assoc CalcsSegmental 2.0 × 20000000 Associated findings Hematoma 3.0 × 00000001Associated findings Nipple retract 3.0 × 00000002 Associated findingsSeroma 3.0 × 00000008 Associated findings Skin involvement 3.0 ×00000010 Associated findings Skin lesion 3.0 × 00000020 Associatedfindings Skin retraction 3.0 × 00000040 Associated findings Skin thicken3.0 × 00000080 Associated findings Trab thicken 3.0 × 00000100 ChangeFrom Prior Incr in size 3.0 × 00000200 Change From Prior Decr in size3.0 × 00000400 Change From Prior Incr in calcs 3.0 × 00002000 ChangeFrom Prior Decr in calcs 3.0 × 00004000 Change From Prior Incr in number3.0 × 00008000 Change From Prior Decr in number 3.0 × 00010000 ChangeFrom Prior Less prom. 3.0 × 00020000 Change From Prior More prom. 3.0 ×00040000 Associated findings Archit distortion 3.0 × 00080000 Associatedfindings Axillary adenop 3.0 × 00100000 Associated findings Chest wallinvas 3.0 × 00200000

The database of ROIs created from all examinations, detailedabnormalities, and pathology is generated and electronically stored atone or more sites. The information is then concatenated. As each examand abnormality's result is created using the bitwise techniquementioned above, a search is made for an identical pathology findingwith the identical set of bitset integer values (lexicon items)describing the abnormalities. If not found, a single record is createdfor each final abnormality pathology finding for each unique set ofinteger “lexicon” values. When duplicates are found, abnormality,benign, and malignant, the appropriate counters are incremented and thedata displayed in profiler button 410 is updated.

In querying the database, the user selects lexicon items and/orpathology findings and the statistical system will instantly show“quick” statistics (total #'s only) in profiler button 410 for otherexam abnormalities that “include” the profile of selected items. Whenthe radiologist selects “round shape” he will instantly see statisticsfor all other abnormalities with a “round shape,” noting how many wereultimately benign, how many were malignant, and how many were neverbiopsied. The radiologist can also view a statistical list of findingsfor all abnormalities with “round shape,” perhaps helping determineprobabilities for malignancy. If the radiologist subsequently alsoselects “spiculated margin,” the same process will occur for allabnormalities with a “round shape” AND a “speculated margin.”

An example embodiment can use a bit-setting method to produce a typicaldatabase that is small enough such that it can be loaded into the mainmemory of the MIS to enable rapid retrieval and updates. In anembodiment, the loading process is performed by a background threadduring system startup allowing the user to continue working duringloading. In querying the database, all the system needs to do is convertthe currently selected lexicon items into their corresponding bitmapvalues, and then search the database using an “exclusive OR” (xor)comparison on the database records. A record matches when all the “set”bit values from the selected items are “set” in the database recordbeing compared. Abnormality, Benign, and Malignant counts on eachmatching record are tabulated and ultimately presented to theradiologist.

The combination of the high-speed statistical comparison database andthe ROI image database allows an embodiment of the system to provide aradiologist with images stored at a local facility for comparativediagnostic purposes. The system also allows a radiologist to selectimages based on the BI-RADS or other lexicon abnormality descriptors,allowing a comparison of additional images from a larger database orfinal pathology results if the abnormality was biopsied. Table 2provides on exemplary mapping of BI-RADS values to the more efficientlystored and searched bit-field values.

TABLE 2 Mammogram Lexicon to BIRADS Conversion and Detailing DATABASEDESCRIPTOR BIT-FIELD ABNORMALITY CLASSIFICATION ID NUMBER VALUE MassIrregular 16 0 × 00000001 Shape Lobulated 190 0 × 00000002 Oval 15 0 ×00000004 Reniform 27 0 × 00000008 Round 14 0 × 00000010 MarginCircumscribed 109 0 × 00000020 Microlobulated 111 0 × 00000040 Obscured28 0 × 00000080 Indistinct 21 0 × 00000100 Spiculated 29 0 × 00000200Intraductal 201 0 × 00000400 Irregular 20 0 × 00000800 Smooth 18 0 ×00001000 Density High density 211 0 × 00002000 Equal density 213 0 ×00004000 Low density 212 0 × 00008000 Fat containing 214 0 × 00010000Cent lucent 215 0 × 00020000 Wall Septated internal wall 25 0 × 00080000Irregular internal wall 24 0 × 00100000 Smooth internal wall 23 0 ×00200000 Thickened wall 199 0 × 00400000 Calcification Type (genericcalcs) 701 0 × 00000001 Amorphous 702 0 × 00000002 Branching 703 0 ×00000004 Coarse 704 0 × 00000008 Dystrophic 705 0 × 00000010 Eggshell706 0 × 00000020 Fine 707 0 × 00000040 Heterogeneous 708 0 × 00000100Indistinct 709 0 × 00000200 Large rodlike 710 0 × 00000400 Layering 7110 × 00000800 Linear 712 0 × 00001000 Lucent-centered 713 0 × 00002000Milk of calcium 714 0 × 00004000 Pleomorphic 715 0 × 00008000 Punctate716 0 × 00010000 Rim 717 0 × 00020000 Round 718 0 × 00040000 Skin 719 0× 00080000 Spherical 720 0 × 00100000 Suture 721 0 × 00200000 Vascular722 0 × 00400000 Calcification Clustered 751 0 × 00800000 DistributionDiffuse 752 0 × 01000000 Grouped 753 0 × 02000000 Linear 754 0 ×04000000 Regional 755 0 × 08000000 Scattered 756 0 × 10000000 Segmental757 0 × 20000000 Foreign body, Hematoma 478 0 × 00000001 Scar, or otherNipple retract 477 0 × 00000002 (typically ignore) Post surgical scar479 0 × 00000004 Seroma 469 0 × 00000008 Skin involvement 252 0 ×00000010 Skin lesion 473 0 × 00000020 Skin retraction 251 0 × 00000040Skin thicken 250 0 × 00000080 Trab thicken 470 0 × 00000100 Changes fromIncr in size 77 0 × 00000200 prior exam Decr in size 78 0 × 00000400Incr in calcs 483 0 × 00002000 Decr in calcs 484 0 × 00004000 Incr innumber (mass) 481 0 × 00008000 Decr in number (mass) 482 0 × 00010000Less prom. 293 0 × 00020000 More prom. 294 0 × 00040000

Detailing window 400 displays information that can be stored as BI-RADScompatible data points, or another suitable lexicon. Optionally the ROIdata can be generated by a CAD software package used to pre-evaluate andcategorize the ROI in the MIS. Detailing window 400 also provides aradiologist with an interface to adjust, re-characterize, correct, orremove the ROI data based on their professional assessment of the ROIdepicted in the patient's images if they radiologist disagrees with theCAD generated results. All of this information can be stored in adatabase configured to correlate all of a patent's ROI data and images.

The features provided by the system can also be combined with any one ofseveral available computer aided diagnostic (CAD) products to validate,improve, and allow simplified testing of future CAD algorithms. A CADproduct can be evaluated by using the electronically compileddescriptions of any abnormalities shown in a collection of ROI images tocompare the CAD software algorithms against the real world pathology orbiopsy results that were actually performed on the ROIs depicted in theimage database.

Once the reliable performance of a CAD algorithm is established it maybe used to further assist or confirm radiologist assessments ofmammography images from new patients, or to alert the medical staff orradiologists when new or previously unclassified abnormalities aredetected. Additionally, the integration of a CAD algorithm and thelexicon abnormality descriptors to generate ROI entries, such as thosedepicted in FIG. 6 b, can pre-select the ROI classifications for eachabnormality detected by a CAD product. This combination is especiallyadvantageous as it reduces the number of radiologist provided entries toonly corrections to the CAD interpretation of an ROI or any ROI thatwere not categorized initially by the CAD product. While a handful ofmouse clicks or keyboard entries, or similar gestures, may seem trivial,the combined time savings over the high volume of patient images thatmust be reviewed can yield a substantial savings in time, cost andcomfort.

In the example embodiment discussed above, the display of thestatistical results in profiler button 410 is automatically updatedevery time the radiologist enters or changes a data point. In anotherembodiment, the statistical results display window or profiler button410 is hidden, or the update suppressed, until the entry of all of thepatient's data is complete. This alternative embodiment may be useful asa training tool for educating new radiologists by preventing them frombeing influenced by the statistical updates as they perform their entryof the data points for a patient.

As shown in FIG. 7, when the user activates, or clicks on, the profilerbutton 410 of FIG. 6 b, a window of matching statistical information 500is displayed. This window of matching statistical information 500includes the individual quantity 502 and the percentages 504 formalignant and benign outcomes in a sorted itemized list with both localand national data based on the matching selected abnormality features.Additionally, window 500 also includes the various pathology findings506, as well as the code for that finding 508, contained in thedatabase.

The example embodiment provides a “show exams” button 510 that allows aradiologist to retrieve the examinations for an individually selectedpathology type 512. FIG. 8 depicts an examination list window 550 forthe selected pathological type 512. The matching exams displayed in FIG.8 are only those database records from the local facility database. Anyrecords retrieved from a non-local database would not contain anypatient identifying information. The embodiment of the MIS depicted herefurther provides the radiologist with the opportunity to select a record560 of individual patient with the same diagnosis 512 for furtherreview. The selection of the “View patient priors” button 570 directsthe system to open a window containing the selected patient'sexamination record and “Send Images to Viewstation” button 572 that canbe selected to send images to display workstation 100 or image monitors112 and 114 that allows the radiologist to view multiple matchingimaging features and pathological outcomes in similar imagingmodalities.

FIG. 9 depicts an exemplary prior exam window 600 displaying the imagesfor an individual patient's exam. Prior exam window 600 includesexisting or historical exam images for the selected patient forreferencing process of care. By selecting an individual exam report 602and then one of the “View Full” 604, “Preview” 606, “Print” 608, or“Send to Viewstation” 610, the radiologist can examine the selected examreport 602 and optionally compare the images contained in that record tothe current patient's images. Additionally, the system allows theradiologist to export a variety of bulk data, such as to a CD or otherlocation with the “Create CD” button 612 option. The bulk data mayinclude all of the images related to a single patient or a collection ofcategorized abnormality images that match a set of selected abnormalityattributes or some other data subset.

FIG. 10 depicts a patient report 700 summarizing the details of the CADor radiologist findings from the examination and analysis of thepatient's images. The report 700 can contain a clipped portion of themedical image or a thumbnail picture summarizing the ROI, as well as amulti-perspective wireframe guide that maps the location of the ROI ontothe outline of the patient's anatomy.

FIG. 11 through FIG. 14 depict an exemplary embodiment of a standaloneor web-based interface 800 to an embodiment of the profiler system. Theweb-based interface 800 can be accessed with any of the commonlyavailable web browsers such as Microsoft Internet Explorer or MozillaFirefox. As appreciated by those skilled in the art, a web-basedinterface may be hosted on a server connected to the Internet for use bya variety of geographically separated individuals or locally whereaccess is limited to a particular facility's local network.

FIG. 11 depicts a web-based interface 800 providing a mechanism toselect various characteristics regarding abnormality informationcontained in a database. Four modalities are presented, Mammogram—Mass802, Mammogram—Calcification 804, MRI 806 and Ultrasound (US) 808.Depending on the modality selected, additional characteristics relatedto the selected modality are displayed to provide further details of theabnormality information request. The example depicted in FIG. 11indicates a request for abnormality information contained in thedatabase where the abnormality is categorized as a Mammogram—Mass 802,has an irregular shape 810, a speculated margin 812, and a high density814. Mammogram—Mass 802 can also have associated calcification types818.

As depicted in FIG. 12, the Mammogram—Calcification 804 modality isselected as the primary abnormality, and the Mass column containing theShape 810, Margin 812, Density 814, and Orientation 816 categories,shown in FIG. 11, are removed from the interface 800. Interface 800 caninclude a results summary display area 820 and a matching pathologydisplay area 840. The results summary display area 820, in a mannersimilar to the profiler button 410 of FIG. 6 a, displays a count ofmatching abnormalities and their pathological outcome that were found inthe database, as well as the percentages of the biopsied abnormalitiesthat we either malignant or benign.

The matching pathology display area 840 can include a list of findingsthat can detail the percentages of a pathology diagnosis forabnormalities that were malignant or benign. The display area 840example includes the result 842 as either malignant or benign, thenumber of entries 844 in the national database, the percentage 846 thateach pathology represents of either the malignant or benign diagnosis, apathology code 848 and a summary of the finding 850. Both the resultssummary display area 820 and the matching pathology display area 840 areupdated whenever a new abnormality categorization is selected.

FIG. 13 depicts an example embodiment of interface 800 displayingcategories that are related to the MRI 806 modality. As shown in the“Percent of” column 852 of the matching pathology display area 840, thepercentages of the abnormality diagnosis are calculated as the number ofrelevant diagnosis from the total number of just the malignant or justthe benign results. As shown, the percentages of malignant diagnosisequal 100% and the benign diagnosis equal 100%.

FIG. 14 depicts an example embodiment of interface 800 displayingcategories that are related to the ultrasound 808 modality. Theultrasound 808 modality includes fields for “Boundary,” “Hilum,” “Echo,”and “Internal Echo” in column 860 that are specific to ultrasoundimaging techniques. It is contemplated that other fields, columns, ormodalities can be added or presented as needed to accommodate thepreferences of the user or to incorporate other new or existingdiagnostic technologies.

FIG. 15 depicts an embodiment of a ROI Gallery 900 containing selectedimage clippings 910 that have been associated with the ROI depicted bythe craniocaudal mark 252. The activation of the “Roi Gallery” button290, shown in FIG. 3, causes the ROI Gallery 900 to be presented to theuser. The image clippings 910 can be selected from any region of amedical image available to the radiologist on the MIS. A lowmagnification image 912 can be useful to identify a large area oftissue. Alternatively, a smaller, higher magnification image 914 canprovide the radiologist with greater detail.

The association of image clippings 910 can allow the radiologist toassociate a variety of images with the set of categories, such as thoseassociated with the ROI of FIG. 3. By correlating a subset of a fullresolution image the radiologist is able to focus on the specific areathat is described by the characteristics. This correlation of ROIcharacteristics with any of a variety of radiologist selected imageclippings 910 can then be used in during future examinations to quicklyfocus in on individual areas that may need review. One example would beclipping a view of an abnormality that the radiologist recommended bereviewed after six or twelve months for any changes in size orappearance.

Additionally, the system provides for the clipping of various modalitiesof images. In addition to the mammogram images as shown in the ROIGallery 900, additional images such as ultrasound or MRI captures canalso be included in the gallery. One embodiment of this system canemploy the storage of individual image clippings 910 in a compressedimage format, such as the JPEG image format established by the JointPhotographic Experts Group, or another appropriate standard. The use ofa compressed image format provides an acceptable resolution for athumbnail image for an initial investigation, while requiring lessstorage space than a high-resolution image format, such as the DICOMformat. The system also provides a link from the compressed imageclippings 910 to the full-sized high-resolution image for thesituations, such as making a diagnostic assessment, that require aradiologist to view the high-resolution image.

In one embodiment of the system, a database of thumbnail or clippedimages can provide a source of investigational data that may assist aradiologist in categorizing an abnormality that he or she is unfamiliarwith, or for use as a training tool. The association of the ROIcategorizations with the clipped images also provides an efficientmechanism to search for individual image clippings 910 of a particulartype of abnormality or to provide a convenient link to pathology reportsor patient correspondence. Non-image based information such as patientcorrespondence or reports can be stored in the ROI Gallery 500 either intheir native format or in an image format, such as JPEG, TIFF, GIF, oranother appropriate standard, derived from a screen-capture of thereport or document.

FIG. 16 a is another depiction of ROI Gallery 900. Image clipping 910,as well as other images, can be attached or associated directly to anabnormality, such as ROI, depicted by the craniocaudal mark 252. FIG. 16b depicts of ROI Gallery 900 with a single highlighted image clipping910 as indicated by highlight-bar 920. Various exemplary tools are shownin ROI Gallery 900 that provide for the manipulation of individual imageclippings. When an image is associated to an abnormality, the title bar920 changes color, indicating a direct association. Tapping the “+” 924provides a mechanism to attach image to abnormality 910. Tapping “−” 926disassociates image clipping 910 if attached to an ROI. A double-clickon image clipping 910 or tapping on magnification button 928 brings upan individual ROI viewer 950 to allow a large view along with access toother imaging tools.

Within the title bar the description of the view is displayed from theimage it was obtained from, for example RCC (RightCranioCaudal) image.In an embodiment, if the image was processed through a CAD tool, thefeature descriptors, such as CAD-generated ROI outlines provided by thattool, are displayed. In another embodiment, feature descriptors can besuperimposed as an overlay on top of the image. Alternatively, ahovering tool bar tool, for example when a user leaves the mouse cursorover an image, provides a small message describing the area.Additionally, in order to reduce right/left errors when associatingimages to an ROI, the imaging gallery does not allow right ROI to beassociated to left breast abnormality, and a left ROI is not allowed tobe associated with a right breast image or abnormality.

As depicted, a user can delete 922 the image clipping 910, or open theimage clipping 910 in an individual ROI viewer upon the selection ofmagnification button 928.

FIG. 17 depicts an example embodiment of a ROI viewer 950 depicting anindividual image 952. The ROI viewer 950 provides additional imagemanipulation tools, including an “invert” selector 954 that replaces theblack pixels for white and the white pixels for black. The ROI viewer950 also provides a “3D” button 956 that can support the activation of aseparate 3D-modeling software package, one example of which is availablefrom Clario, that enables the radiologist to view and rotate a compositethree-dimensional image of the associated ROI. The radiologist mayreturn to the ROI Gallery 900 by selecting either the “Exit” button 958or the “Close Window” icon 960.

FIG. 18 is an example of a patient work-list form 1000 for use withembodiments of this invention. The work-list form 1000 allows the systemto coordinate the retrieval of any high-resolution images in order toeffectively utilize network bandwidth and system storage capacity. FIG.19 is an example of a prior examinations form 1100 for use withembodiments of this invention. The prior examination form 1100 providesa radiologist with convenient access to a patient's prior medical imagefor review or comparison with a more current set of images.

An example embodiment of a diagnostic system can provide a user, orimage interpreter, with a simultaneous view of a region of interest(ROI) in an image along with options for the user to include or discardthe ROI through an automated sequence, select or confirm suggestedattributes and features describing any abnormalities in the ROI, and togenerate a report detailing the content of the image. Attributes andfeatures describing any abnormalities in the ROI can be generated byperforming a feature extraction process on an image, are displayed foruser review as hints or suggestions. An exemplary hint can include anattribute designated from a plurality of possible attributes byhighlighting the attribute with a different color, font, or format, thanany non-highlighted attribute. The hints, or attribute suggestions, canindicate one or more of a variety of possible attribute descriptionsthat describe a specific feature of the ROI in the image. The automatichighlighting or hinting by the diagnostic system can aid a user,typically a radiologist, in quickly assessing the region of interest andselecting an appropriate description attributes for the ROI. The usercan accept, modify, or reject any of the suggested attributes. Once theuser is satisfied with the selected descriptions, incorporating eitherthe suggested attributes or any user-selected description attributes, areport detailing the one or more regions of interest in the image can beautomatically generated.

Attribute descriptions can include, for example, the location,attributes, and size of an abnormality within the region. The system canprovide a user with the ability to select one or more highlighted hints,or select other descriptive attributes to be included in report. Allhints can be selected by a common action (e.g., a “select all” optionbutton or an appropriate voice command). All hints can alternatively bediscarded or suppressed by a common action (e.g., a “deselect all”option button or an appropriate voice command).

In one embodiment, an automated diagnostic reporting system can becontrolled by a voice recognition module, to select or deselect featuresand discard or include selected regions, without interacting with akeyboard or a pointing device (e.g., a mouse). A word library of voicecommands can be limited to specific ROI or abnormality related terms toincrease accuracy of recognition. Additionally, the system can haveinternal libraries to facilitate other keywords, slang or abbreviationsthat are user selected or germane to a particular image being analyzed.The entry of commands can be confirmed with or without computer voicefeedback.

In one embodiment, the system can record both the features indicated byimaging processing software module as well as the interpretations ofeach image selected by a human user. The combination of the imageprocessing software selections and the user selections can be packagedtogether for further statistical processing or analysis. The packagedsoftware and user originated selection package can optionally includethe image or ROI selected from the image.

In one embodiment, an automated diagnostic reporting system can performan automatic import process that displays a ROI abnormalitysimultaneously along with the features extracted for that abnormality.In addition to the automatic processing mode a mechanism is provided toallow a radiologist to manually iterate through each of the regions orattributes as the data is imported from an automated diagnosticanalysis. If a user selects the manual mode of operation a spreadsheetcan provide a brief description for each region of interest.

FIG. 20 illustrates an example embodiment of a medical diagnosticreporting system that includes an abnormality-summary window orexamination screen 1200. A user is presented examination screen 1200after the selection of an exam. The automatic sequencing of the ROI canbe initialized by the user selection of an “Auto-Next” feature 1202, bymanually selecting a ROI on spreadsheet list 1204 containing textdescriptors, or by selecting a ROI icon 1206 superimposed on graphicalillustration 1208 reflecting the general position of the ROI on thepatient. Upon selection of any of these methods the user can bepresented with a detail screen 1300 displaying various descriptorsdescribing the ROI in more detail, such as the example as depicted inFIG. 21.

In one embodiment the diagnostic reporting system can automaticallyextract the data pertaining to the image and a ROI in the image, anddisplay the location on the title bar 1302 of screen 1300, and on thegraphical diagram, as well as the suggestive feature set. A diagnosticreporting system can provide for the selection of keywords to buildcomplete sentences for a report based on items extracted from theimaging data file and/or from items selected from the detail screen todescribe features.

As depicted in FIG. 21, highlighted items 1306, which can be shown in adashed box or alternatively displayed with a highlighted color (or otherfont or format), are imported from the data file associated with theimage, processed, and are presented to the user as a suggestion or hint,or alternatively, presented as a selected item 1304, based on the userspreferences. The user can select individual items (1304, 1306, 1308) tobe included in a report by clicking or tapping on the item with a mouseor other pointing device, or by a voice activation command. Individualselections can be toggled on or off, to be included or excluded for areport, by clicking/tapping on the selection or repeating a voicecommand associated with the selection. The item is highlighted, orotherwise indicated by colored boarder or formatting, upon voiceconfirmation or the final generation of a report. The imaging viewingscreen 1400 can include one or more images 1402 associated with the ROIabnormality 1404.

In one embodiment, individual items (1304, 1306, 1308) can behighlighted in a first color, for example yellow, to indicate asuggested ROI or image attribute as determined by an imaging processingsoftware module or CAD tool. A second color, for example blue, canindicate that a user has selected or confirmed individual items. In thismanner the diagnostic reporting system can indicate to the use whichattributes are automatically generated by the system and whichattributes were user selected.

In one embodiment, as allowed by some medical regulatory agencies, someor all of the attribute descriptions as generated by a software moduleor CAD tool can be presented to the user in the second color, indicatingthat no user modification or confirmation of the selection is necessary.The user can still change the attribute description regardless of thesystem suggestion. For example, if a CAD tool is approved by anappropriate medical regulatory agency to present the size andorientation of an abnormality depicted in a ROI the size and orientationof an abnormality can be presented in the second color, while all otherCAD tool generated hints or suggestions are highlighted in the firstcolor.

In one embodiment the system can suggest more than one abnormalitydescription and require the user to select one of the two possibleattributes, or an alternate third-attribute, before a report isgenerated. For example, item 1308 indicates that the CAD tool has hintedthat abnormality 1404 should likely be categorized as either “4Suspicious” or “5 Highly Suggestive.” Before a report can be generatedthe user must select one of the attributes related to item 1308,indicating whether the user believes that abnormality 1404 is benign,probably benign, suspicious, highly suggestive, needs additionalevaluation, or an alternate attribute known to those skilled in the art.In one embodiment multiple abnormality descriptions can be highlightedin the first color, prompting the user to select one of the presentedabnormality descriptions. The user selected abnormality description willthen be highlighted by the system in the second color.

In one embodiment a user can attach one or more images associated with aROI in the report automatically by using the “OK-import” action. Onlyimages indicated by highlighted border, which can be a default or userselectable setting, will be attached to the report. Images can beexcluded by tapping on the image to deselect it, such as in the case ofwhen multiple images of various planes are captured and may not berelevant to the report. The user can select “OK” to complete thedescription of ROI and then move to next ROI. If the user decides to notinclude a ROI in report user can select the “cancel” option. The usercan use the profiler statistics window 1310, located in the lower righthand side of screen 1300 in FIG. 21, as a guide to compare theattributes of the displayed ROI with other, previously analyzedabnormalities with matching descriptors for reference on the probabilityof malignancy. The information displayed in profiler statistics window1310 can include information from local or national databases ofpreviously analyzed abnormalities, and present an indication of thenumber of cases with similar attributes that have previously beendiagnosed as malignant or benign.

In one embodiment of the system, the user is presented with an image1500 comprising patient tissue 1502 with a potential abnormality 1504.The user can select an area of a ROI by marking, or indicating with abounding box 1508, a portion of the image area to be analyzed. Thesystem can apply different analyzing software to scale and process theimage 1500, and output an analysis that includes the boundaries andother attributes of the abnormality 1504.

As depicted in FIG. 22 a user can mark a relatively small seed-area 1508within the boundary of an abnormality 1504. The analyzer, such as a CADtool, uses the seed-area mark 1508 within the abnormality as a startingpoint to then locate the boundaries of the abnormality 1504. The imagegallery image can then depict an automatic boundary outline depictingthe complete abnormality 1504 by using one type of a CAD by placement ofa “seed.” The automatic boundary outline of the abnormality isdetermined by expanding the selected “seed” area until it surrounds theentirety of the abnormality 1504.

Alternatively, as depicted in FIG. 23, a user can draw a bounding box1510 with an area greater then a generally small seed-area 1508 mark,potentially encompassing the all or the majority of a visibleabnormality 1504. The CAD tool can then utilize an alternative analyzingcode to determine the boundary the ROI by shrinking the selectedboundary outline of the bounding box 1510. The resulting image with theselected ROI can be output to a gallery as outlined by bounding box 1510or a larger area containing the complete abnormality area if theabnormality extends outside the selected bounding box area.

As shown by the preceding examples, the invention provides an integratedsystem and methods for the categorization, storage, retrieval, andcorrelation of a wide variety of patient data, diagnostic images frommultiple imaging sources, test results, statistics and correspondence.The integration of a ROI profiler, a statistical analysis tool, and thegallery of clipped images, together with native high-resolution medicalimages provides radiologists and other medical professionals with acustomizable tool that provides greater efficiencies while alsoimproving the accuracy of patient diagnostic screenings.

The foregoing descriptions present numerous specific details thatprovide a thorough understanding of various embodiments of theinvention. It will be apparent to one skilled in the art that variousembodiments, having been disclosed herein, may be practiced without someor all of these specific details. In other instances, known componentshave not been described in detail in order to avoid unnecessarilyobscuring the present invention. It is to be understood that even thoughnumerous characteristics and advantages of various embodiments are setforth in the foregoing description, together with details of thestructure and function of various embodiments, this disclosure isillustrative only. Other embodiments may be constructed thatnevertheless employ the principles and spirit of the present invention.Accordingly, this application is intended to cover any adaptations orvariations of the invention. It is manifestly intended that thisinvention be limited only by the following claims and equivalentsthereof.

For purposes of interpreting the claims for the present invention, it isexpressly intended that the provisions of Section 112, sixth paragraphof 35 U.S.C. are not to be invoked with respect to a given claim unlessthe specific terms “means for” or “step for” are recited in that claim.

Any incorporation by reference of documents above is limited such thatno subject matter is incorporated that is contrary to the explicitdisclosure herein. Any incorporation by reference of non-prioritydocuments above is further limited such that no claims included in thedocuments are incorporated by reference herein and any definitionsprovided in the documents are not incorporated by reference hereinunless expressly included herein.

1. A configurable mammography diagnostic system, comprising: a pluralityof electronic displays, at least one of the plurality of electronicdisplays configured to display a breast tissue image; a graphical userinterface presented on at least one of the plurality of electronicdisplays and including: a breast tissue image on which at least oneregion of interest in the breast tissue image can be indicated, aplurality of possible characteristics according to which an automatedcategorization of a region of interest in the breast tissue image can beindicated, and a profiler display configured to present statisticalinformation related to a comparison of the automated categorization withthe existing categorizations; and a processing engine including acomputer aided diagnosis (CAD) tool configured to identify the at leastone region of interest in the breast tissue image, to provide theautomated categorization of the region of interest, and to present theautomated categorization of the region of interest via the graphicaluser interface.
 2. The system of claim 1, wherein the breast tissueimage is selected from the group consisting of an X-ray image, a CTimage, an MRI image, an ultrasound image, and a pathology image.
 3. Thesystem of claim 1, wherein each of the plurality of breast tissue imagesis cataloged in the database according to a characteristic of the breasttissue image.
 4. The system of claim 1, wherein the plurality ofpossible characteristics includes an abnormality type, size, andprofile.
 5. The system of claim 1, wherein the computer aided diagnosis(CAD) tool is further configured to identify the plurality of possiblecharacteristics of an abnormality depicted in the at least one region ofinterest.
 6. The system of claim 1, wherein the computer aided diagnosis(CAD) tool is further configured to identify the at least one region ofinterest in the breast tissue image based on a user selection of aportion of the breast tissue image within the region of interestdepicted in the breast tissue image.
 7. The system of claim 1, whereinthe computer aided diagnosis (CAD) tool is further configured toidentify the at least one region of interest in the breast tissue imagebased a user selection of a portion the breast tissue image containingthe region of interest depicted in the one of the breast tissue image.8. The system of claim 1, further comprising an interface to receive amanual re-categorization of the region of interest.
 9. The system ofclaim 8, wherein the processing engine is operable to compare theautomated categorization and the manual re-categorization of the regionof interest.
 10. The system of claim 9, wherein an algorithm of the CADtool is updated based on the comparison of the CAD automatedcategorization with the manual re-categorization.
 11. The system ofclaim 8, wherein the processing engine is operable to compare the CADautomated categorization with pathological data related to a particularregion of interest.
 12. A method for managing patient mammography datacomprising: obtaining a plurality of breast tissue images selected fromthe group consisting of an X-ray image, a CT image, an MRI image, anultrasound image, and a pathology image; identifying a region ofinterest in at least one of the plurality of breast tissue images;obtaining, from a computer aided diagnosis (CAD) tool, a categorizationof a breast tissue abnormality in the region of interest according to anestablished lexicon; presenting the categorization of the breast tissueabnormality in the region of interest to a user; receiving aconfirmation of the categorization from the user; and associating theconfirmed categorization with the region of interest.
 13. The method ofclaim 12, further comprising: receiving a modification of thecategorization from the user.
 14. The method of claim 12, furthercomprising: comparing the confirmed categorization of the region ofinterest with a database of existing categorizations.
 15. The method ofclaim 14, further comprising: presenting a diagnostic indicatorcomprising a number of instances of the categorization within a databaseof existing categorizations, a number of diagnoses of malignantabnormalities for the categorization among the existing categorizations,and a number of diagnoses of benign abnormalities for the categorizationamong the existing categorizations.
 16. The method of claim 12, furthercomprising: updating the diagnostic indicator based on a comparison ofthe confirmed categorization and the existing categorizations.
 17. Themethod of claim 16, further comprising: comparing the usercategorization with the computer aided diagnosis (CAD) categorizationfor the same region of interest.
 18. The method of claim 14, wherein thedatabase of existing categorizations includes an identified region ofinterest in each of a plurality of breast tissue images, and whereineach region of interest is associated with one of either a malignant ora benign diagnosis.
 19. The method of claim 18, further comprising:comparing the associated malignant or benign diagnosis of a region ofinterest in the database with a computer aided diagnosis (CAD) of theone of the plurality of breast tissue images associated with the regionof interest.
 20. A method for managing patient medical data comprising:obtaining a plurality of tissue images selected from the groupconsisting of an X-ray image, a CT image, an MRI image, an ultrasoundimage, and a pathology image; storing the plurality of tissue images inan image database having a non-volatile computer readable mediumconfigured to retrievably store the plurality of tissue images;performing a digital image analysis of the plurality of tissue images;identifying a region of interest in at least one of the tissue images;performing a categorization of a tissue abnormality in the region ofinterest according to an established lexicon with a computer aideddiagnosis (CAD) tool; presenting the categorization of the tissueabnormality in the region of interest to a user; and presenting the userwith statistical information regarding the categorization.
 21. Themethod of claim 20, further comprising: receiving a modification of thecategorization from the user.
 22. The method of claim 20, furthercomprising: comparing the categorization with a database including aplurality of diagnosed tissue abnormality categorizations.
 23. Themethod of claim 20, wherein the identification of a region of interestin at least one of the tissue images includes a user selection of aportion of one of the tissue images within the region of interestdepicted in the one of the tissue images.
 24. The method of claim 20,wherein the identification of a region of interest in at least one ofthe tissue images includes a user selection of a portion of one of thetissue images containing the region of interest depicted in the one ofthe tissue images.
 25. The method of claim 20, wherein theidentification of a region of interest in at least one of the tissueimages includes identifying a region of interest in at least one of thetissue images based on the digital image analysis.